DBMS > Apache Impala vs. GeoSpock vs. MySQL vs. Solr vs. Warp 10

System Properties Comparison Apache Impala vs. GeoSpock vs. MySQL vs. Solr vs. Warp 10

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Spatial and temporal data processing engine for extreme data scale

Widely used open source RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Key/Value like access via memcached API

Search engine

Time Series DBMS

Secondary database models

Document store

Time Series DBMS

Document store
Spatial DBMS

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

geospock.com

Technical documentation

impala.apache.org/impala-docs.html

dev.mysql.com/doc

solr.apache.org/resources.html

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

GeoSpock

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

SenX

Initial release

2013

1995

2006

2015

Current release

4.1.0, June 2022

2.0, September 2019

8.4.0, April 2024

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Java, Javascript

C and C++

Java

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

temporal, categorical

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI SQL for query only (using Presto)

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Java plugins

yes

WarpScript

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per table

Users with fine-grained authorization concept

no user groups or roles

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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